Site News  |   Monitor Keywords  |   Monitor Archive  |   Organizer  |   Account Info  |

Genetic models for stratification of cancer risk

Genetic models for stratification of cancer risk description/claims

The present application claims benefit of priority to U.S. Provisional Application Ser. No. 60/949,172, filed Jul. 11, 2007 and U.S. Provisional Application Ser. No. 60/951,110, filed Jul. 20, 2007, the entire contents of both which are hereby incorporated by reference.

The government owns rights in the present invention pursuant to grant number DAMD17-01-1-0358 from the United States Army Breast Cancer Research Program, and grant numbers AR992-007, AR01.1-050 and AR05.1025 from the Oklahoma Center for the Advancement of Science and Technology (OCAST).

1. Field of the Invention

The present invention relates generally to the fields of oncology and genetics. More particularly, it concerns use of multivariate analysis of genetic alleles constituting genotypes to determine genotypes and combinations of genotypes associated with low, intermediate and high risk of particular cancers. These risk alleles are used to screen patient samples, evaluation of incremental and lifetime risk of developing cancer, and efficiently direct patients towards prediagnostic cancer risk management and prophylaxis.

2. Description of Related Art

For patients with cancer, early diagnosis and treatment are the keys to better outcomes. In 2001, there are expected to be 1.25 million persons diagnosed with cancer in the United States. Tragically, in 2001 over 550,000 people are expected to die of cancer. To a very large extent, the difference between life and death for a cancer patient is determined by the stage of the cancer when the disease is first detected and treated. For those patients whose tumors are detected when they are relatively small and confined, the outcomes are usually very good. Conversely, if a patient's cancer has spread from its organ of origin to distant sites throughout the body, the patient's prognosis is very poor regardless of treatment. The problem is that tumors that are small and confined usually do not cause symptoms. Therefore, to detect these early stage cancers, it is necessary to continually screen or examine people without symptoms of illness. In such apparently healthy people, cancers are actually quite rare. Therefore it is necessary to screen a large number of people to detect a small number of cancers. As a result, annual or regularly administered cancer-screening tests are relatively expensive to administer in terms of the number of cancers detected per unit of healthcare expenditure.

A related problem in cancer screening is derived from the reality that no screening test is completely accurate. All tests deliver, at some rate, results that are either falsely positive (indicate that there is cancer when there is no cancer present) or falsely negative (indicate that no cancer is present when there really is a tumor present). Falsely positive cancer screening test results create needless healthcare costs because such results demand that patients receive follow-up examinations, frequently including biopsies, to confirm that a cancer is actually present. For each falsely positive result, the costs of such follow-up examinations are typically many times the costs of the original cancer-screening test. In addition, there are intangible or indirect costs associated with falsely positive screening test results derived from patient discomfort, anxiety and lost productivity. Falsely negative results also have associated costs. Obviously, a falsely negative result puts a patient at higher risk of dying of cancer by delaying treatment. To counter this effect, it might be reasonable to increase the rate at which patients are repeatedly screened for cancer. This, however, would add direct costs of screening and indirect costs from additional falsely positive results. In reality, the decision on whether or not to offer a cancer screening test hinges on a cost-benefit analysis in which the benefits of early detection and treatment are weighed against the costs of administering the screening tests to a largely disease free population and the associated costs of falsely positive results. In addition, many advanced screening and imaging methods exist that are more accurate than general screening tests, but the costs for administering these tests using these advanced imaging tools is many times more expensive.

Another related problem concerns the use of chemopreventative drugs for cancer. Basically, chemopreventatives are drugs that are administered to prevent a patient from developing cancer. While some chemopreventative drugs may be effective, such drugs are not appropriate for all persons because the drugs have associated costs and possible adverse side effects (Reddy and Chow, 2000). Some of these adverse side effects may be life threatening. Therefore, decisions on whether to administer chemopreventative drugs are also based on a risk-benefit analysis. The central question is whether the benefits of reduced cancer risk outweigh the associated drug risks and costs of the chemopreventative treatment.

The risk-benefit balance has to be favorable for prescribing a preventative drug and it is not favorable for an individual who is not at increased risk for developing cancer, where it is for an individual who is at increased risk. One problem is being able to effectively identify individuals that are at higher-than-average risk for developing cancer.

Currently, an individual's age is the most important factor in determining if a particular cancer-screening test should be offered to a patient. Truly, cancer is a rare disease in the young and a fairly common ailment in the elderly. The problem arises in screening and preventing cancers in the middle years of life when cancer can have its greatest negative impact on life expectancy and productivity. In the middle years of life, cancer is still fairly uncommon. Therefore, the costs of cancer screening and prevention can still be very high relative to the number of cancers that are detected or prevented. Decisions on when to begin screening also may be influenced by personal history or family history measures. Unfortunately, appropriate informatic tools to support such decision-making are not yet available for most cancers.

A common strategy to increase the effectiveness and economic efficiency of cancer screening and chemoprevention in the middle years of life is to stratify individuals' cancer risk and focus the delivery of screening and prevention resources on the high-risk segments of the population. Two such tools to stratify risk for breast cancer are termed the Gail Model and the Claus Model (Costantino et al., 1999; McTiernan et al., 2001). The Gail model is used as the “Breast Cancer Risk-Assessment. Tool” software provided by the National Cancer Institute of the National Institutes of Health on their web site. Neither of these breast cancer models utilizes genetic markers as part of their inputs. Furthermore, while both models are steps in the right direction, neither the Gail nor Clause models have the desired predictive power or discriminatory accuracy to truly optimize the delivery of breast cancer screening or chemopreventative therapies.

These issues and problems could be reduced in scope or even eliminated if it were possible to stratify or differentiate a given individual's risk from cancer more accurately than is now possible. If a precise measure of actual risk could be accurately determined, it would be possible to concentrate cancer screening and chemopreventative efforts in that segment of the population that is at highest risk. With accurate stratification of risk and concentration of effort in the high-risk population, fewer screening tests or more advanced screening tests that may be more expensive would be directed toward the higher risk segment of individuals to detect a greater number of cancers at an earlier and more treatable stage. Fewer screening tests would mean lower test administrative costs and fewer falsely positive results. A greater number of cancers detected would mean a greater net benefit to patients and other concerned parties such as health care providers. Similarly, chemopreventative drugs would have a greater positive impact by focusing the administration of these drugs to a population that receives the greatest net benefit.

Thus, in accordance with the present invention, there is provided a method for assessing a female subject's risk for developing breast cancer comprising determining, in a sample from the subject, the allelic profile of more than one SNP selected from the group consisting of ACACA (IVS17) T→C, ACACA (5′UTR) T→C, ACACA (PIII) TAG, COMT (rs4680) A→G, CYP19 (rs10046) T→C, CYP1A1 (rs4646903) T→C, CYP1B1 (rs 1800440) A→G, EPHX (rs1051740) T→C, TNFSF6 (rs763110) C→T, IGF2 (rs2000993) G→A, INS (rs3842752) C→T, KLK10 (rs3745535) G→T, MSH6 (rs3136229) G→A, RAD51L3 (rs4796033) G→A, XPC (rs2228000) C→T, and XRCC2 (rs3218536) G→A, including 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 or 22 SNPs in 19 genes. The method may further comprise determining the allelic profile of at least one additional SNP selected from the group consisting of CYP11B2 (rs1799998) T→C, CYP1B1 (rs10012) C→G, ESR1 (rs2077647) T→C, SOD2 (aka MnSOD, rs1799725) T→C, VDR (rs7975232) T→G, and ERCC5 (rs17655) G→C.

The method may also further comprise assessing one or more aspects of the subject's personal history, such as age, ethnicity, reproductive history, menstruation history, use of oral contraceptives, body mass index, alcohol consumption history, smoking history, exercise history, diet, family history of breast cancer or other cancer including the age of the relative at the time of their cancer diagnosis, and a personal history of breast cancer, breast biopsy or DCIS, LCIS, or atypical hyperplasia. Age may comprise stratification into a young age group of age 30-44 years, middle age group of age 45-54 years, and an old age group of 55 years and older. Alternatively, age may comprising stratification in 30-49 years and 50-69 years, or 50 and older.

The step of determining the allelic profile may be achieved by amplification of nucleic acid from the sample, such as by PCR, including chip-based assays using primers and primer pairs specific for alleles of the genes. The method may also further comprising cleaving the amplified nucleic acid. Samples may be derived from oral tissue collected by lavage or blood. The method may also further comprise making a decision on the timing and/or frequency of cancer diagnostic testing for the subject; and/or making a decision on the timing and/or frequency of prophylactic cancer treatment for the subject.

In another embodiment, there is provided a nucleic acid microarray comprising nucleic acid sequences corresponding to genes at least one of the alleles for each of ACACA (IVS17) T→C, ACACA (5′UTR) T→C, ACACA (PIII) T→G, COMT (rs4680) A→G, CYP19 (rs10046) T→C, CYP1A1 (rs4646903) T→C, CYP1B1 (rs1800440) A→G, EPHX (rs1051740) T→C, TNFSF6 (rs763110) C→T, IGF2 (rs2000993) G→A, INS (rs3842752) C→T, KLK10 (rs3745535) G→T, MSH6 (rs3136229) G→A, RAD51L3 (rs4796033) G→A, XPC (rs2228000) C→T, and XRCC2 (rs3218536) G→A. The nucleic acid sequences may comprise sequences for both alleles for each of the genes.

In still yet another embodiment, there is provided a method for determining the need for routine diagnostic testing of a female subject for breast cancer comprising determining, in a sample from the subject, the allelic profile of more than one SNP selected from the group consisting of ACACA (IVS17) T→C, ACACA (5′UTR) T→C, ACACA (PIII) T+G, COMT (rs4680) A→G, CYP19 (rs10046) T→C, CYP1A1 (rs4646903) T→C, CYP1B1 (rs1800440) A→G, EPHX (rs1051740) T→C, TNFSF6 (rs763110) C→T, IGF2 (rs2000993) G→A, INS (rs3842752) C→T, KLK10 (rs3745535) G→T, MSH6 (rs3136229) G→A, RAD51L3 (rs4796033) G→A, XPC (rs2228000) C→T, and XRCC2 (rs3218536) G→A.

• Provisional Patent
• Utility Patent

• What Is a Patent?
• What Is a Trademark or Servicemark?
• What Is a Copyright?
• Patent Laws